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Center for Education and Research in Information Assurance and Security

Panel #1: Visualization of Security (Symposium Summary)

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Tuesday, March 30, 2010

Panel Members:

  • Steve Dill, Lockheed Martin
  • Donald Robinson, Northrop Grumman
  • Ross Maciejewski, Purdue
  • Alok Chaturvedi, Purdue

Summary by Ryan Poyar

The first panel of the 2010 annual security symposium kicked things off to a great start and an interesting discussion. The topic was the Visualization of Security. The focus of the panel was to address the issue of how to use the vast amounts of data that is available in a way that can help predict and protect systems from future threats. Alok Chaturvedi, a professor at Purdue, initiated the discussion by describing how using visualization could potentially make it possible to display large amounts of data in a meaningful way. Donald Robinson from Northrop Grumman rationalized the use of using visualization with his argument that as humans we are naturally very good at recognizing patterns and making sense of visualizations as opposed to dealing with raw data. Currently, this technique is being researched through the project VACCINE (Visual Analytics for Command, Control, and Interoperability Environments) which is primarily focused on helping the mission of the Department of Homeland Security. As one of the researchers working on VACCINE, Ross Maciejewski described that the goal of the project was to be able to determine potential threats from an abundance of streaming real-time data sources and then further to provide real-time targeted counter measures against each threat. While all of this sounds very good in theory, getting it to work in practice requires many hurdles to be overcome. The discussion for the remainder of the panel was a debate on who should be responsible for making the threat determination from the data and then who should determine the correct response. Even in a non-real-time environment with only humans this is a very tricky endeavor. It seems that it is necessary for a specific expert in each field to analyze the data from their perspective and look for threats based on their expertise only. If a threat is found, it is then very difficult to determine who has the right background and is the best choice to mitigate it. Further, who has the ability to foresee threats that cross multiple disciplines? If we have a difficult time answering these questions in a detailed, comprehensive, non-real-time environment how will we be able to design a system a priori that can answer future questions in real-time?

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